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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/14443

Title: Handling of missing data in long-term clinical trials: a case study
Authors: Janssens, Mark
Molenberghs, Geert
Kerstens, René
Issue Date: 2012
Citation: PHARMACEUTICAL STATISTICS, 11 (6), p. 442-448
Abstract: Missing data in clinical trials is a well-known problem, and the classical statistical methods used can be overly simple. This case study shows how well-established missing data theory can be applied to efficacy data collected in a long-term open-label trial with a discontinuation rate of almost 50%. Satisfaction with treatment in chronically constipated patients was the efficacy measure assessed at baseline and every 3?months postbaseline. The improvement in treatment satisfaction from baseline was originally analyzed with a paired t-test ignoring missing data and discarding the correlation structure of the longitudinal?data. As the original analysis started from missing completely at random assumptions regarding the missing data process, the satisfaction data were re-examined, and several missing at random (MAR) and missing not at random (MNAR)?techniques resulted in adjusted estimate for the improvement in satisfaction over 12?months. Throughout the different sensitivity analyses, the effect sizes remained significant and clinically relevant. Thus, even for an open-label trial design, sensitivity analysis, with different assumptions for the nature of dropouts (MAR or MNAR) and with different classes of models (selection, pattern-mixture, or multiple imputation models), has been found useful and provides evidence towards the robustness of the original analyses; additional sensitivity analyses could be undertaken to further qualify robustness.
Notes: [Janssens, Mark; Kerstens, Rene] Shire Movetis NV, B-2300 Turnhout, Belgium. [Molenberghs, Geert] Univ Hasselt, I BioStat, B-3590 Diepenbeek, Belgium. [Molenberghs, Geert] Kaholieke Univ Leuven, B-3590 Diepenbeek, Belgium.
URI: http://hdl.handle.net/1942/14443
DOI: 10.1002/pst.1532
ISI #: 000310789600002
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2013
Appears in Collections: Research publications

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